Digital Pre-Distortion (“DPD”) is a signal processing technique, which is widely used to mitigate nonlinearity effects of power amplifiers, such as for example in third and fourth generation wireless transmitters. DPD is used to precondition an input signal to counter nonlinearity effects of a power amplifier (“PA”) in wireless systems, so output of the power amplifier is more linear than without such DPD of the input signal. Generally, it is more cost effective to digitally predistort an input signal to a power amplifier than to make the power amplifier output linear over a wide bandwidth of operation. Some of such wireless transmitters support Multiple-Input, Multiple-Output (“MIMO”) operation in combination with spatial multiplexing, such as for example in various wireless communication specifications including 3GPP LTE and IEEE 802.11n. Thus, multiple antennas may be used for transmitting in such systems.
A DPD system may include of a filter that predistorts data samples (“samples”) prior to input to a PA, and a parameter estimator, coupled as part of a feedback path from an output of such PA to a digital predistorter (“predistorter”) for digital predistortion may be used to update predistortion coefficients used by such predistorter. Unfortunately, during operation, such parameter estimator, in order to quickly adapt predistortion coefficients to changing conditions of such PA, parameters associated with large matrices are regularly updated, which is resources intensive in order to meet such changing conditions. Faster updating for a wireless system may translate into supporting more antennas. Along those lines, it is better to have low update times to allow support for more antennas using one parameter estimator. To obtain such low update times, heretofore complexity of a predistorter has been increased by increasing the number of parameters (i.e., the number of coefficients) processed. However, this may add cost and/or increase power consumption.
Accordingly, it would be useful to provide for lower update times while avoiding one or more of the issues associated with more complex parameter estimators.